Background suppression for infrared dim small target scene based on adaptive gradient reciprocal filtering

Li Biao; Xu Zhiyong; Wang Chen; Zhang Jianlin; Wang Xiangru; Fan Xiangsuo

doi:10.12086/oee.2021.210122

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Li B, Xu Z Y, Wang C, et al. Background suppression for infrared dim small target scene based on adaptive gradient reciprocal filtering[J]. Opto-Electron Eng, 2021, 48(8): 210122. doi: 10.12086/oee.2021.210122

Citation:

Li B, Xu Z Y, Wang C, et al. Background suppression for infrared dim small target scene based on adaptive gradient reciprocal filtering[J]. Opto-Electron Eng, 2021, 48(8): 210122. doi: 10.12086/oee.2021.210122

Background suppression for infrared dim small target scene based on adaptive gradient reciprocal filtering

1.
Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China
2.
School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
3.
University of Chinese Academy of Sciences, Beijing 100049, China
4.
Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China
5.
Chengdu Office of Military Agency Bureau of Equipment Department, Aerospace System Ministry, Chengdu, Sichuan 610041, China
6.
School of Electrical and Information Engineering, Guangxi University of Science and Technology, Liuzhou, Guangxi 545006, China

Fund Project: National Natural Science Foundation of China (62001129), the West Light Foundation of the Chinese Academy of Sciences (ya18k001), and the Guangxi Science and Technology Base and Talent Project (2019AC20147)

More Information

^*Corresponding author: Zhang Jianlin, E-mail: jlin_zh@163.com

Received Date 15 April 2021

Revised Date 30 June 2021

Published Date 15 August 2021

Abstract

Abstract

Due to the small scale and weak energy of the infrared dim small target, the background must be suppressed to enhance the target in order to ensure the performance of detection and tracking of the target in the later stage. In order to improve the ability of gradient reciprocal filter to suppress the clutter texture and reduce the interference of the residual texture to the target in the difference image, an adaptive gradient reciprocal filtering algorithm (AGRF) is proposed in this paper. In the AGRF, the adaptive judgment threshold and the adaptive relevancy coefficient function of inter-pixel correlation in the local region are determined by analyzing the distribution characteristics and statistical numeral characteristic of the background region, clutter texture, and target. Then the element value of the adaptive gradient reciprocal filter is determined by combining the relevancy coefficient function and the gradient reciprocal function. Experimental results indicate that the sensitivity of the AGRF algorithm to the clutter texture is significantly lower than that of the traditional gradient reciprocal filtering algorithm under the premise of the same target enhancement performance. Compared with the other nine algorithms, the AGRF algorithm has better signal-to-noise ratio gain (SNRG) and background suppress factor (BSF).
- infrared dim small target /
- background suppression /
- gradient reciprocal filtering /
- clutter texture

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References

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Overview

Overview

Overview: Due to the small scale and weak energy of the infrared dim small target, the background must be suppressed to enhance the target in order to ensure the performance of detection and tracking of the target in the later stage. In order to improve the ability of gradient reciprocal filter to suppress the clutter texture and reduce the interference of the residual texture to the target in the difference image, an adaptive gradient reciprocal filtering algorithm (AGRF) is proposed in this paper. In the AGRF, the adaptive judgment threshold and the adaptive relevancy coefficient function of inter-pixel correlation in the local region are determined by analyzing the distribution characteristics and statistical numeral characteristic of the background region, clutter texture, and target. Then the element value of the adaptive gradient reciprocal filter is determined by combining the relevancy coefficient function and the gradient reciprocal function. Experimental results indicate that the sensitivity of the AGRF algorithm to the clutter texture is significantly lower than that of the traditional gradient reciprocal filtering algorithm under the premise of the same target enhancement performance. Compared with the other nine algorithms, the AGRF algorithm has better signal-to-noise ratio gain (SNRG) and background suppress factor (BSF).

Compared with the traditional gradient reciprocal filtering algorithm, the AGRF algorithm has the following characteristics: 1) The parameters are fully adaptive. The AGRF algorithm provides a new threshold determination method for the inter-pixel correlation, which realizes the adaptive determination of the threshold with the statistical features of the neighborhood pixels. A new correlation coefficient function is defined to improve the gating performance of the filter by its value nonlinear adaptive change with the correlation coefficient. 2) Compared with the traditional reciprocal gradient filtering algorithm, the AGRF algorithm can effectively suppress the background with better texture suppression. Compared with the traditional reciprocal gradient filtering algorithm, the parameters of the AGRF algorithm can be adjusted completely adaptively according to the statistical characteristics of image components with different distribution characteristics, so it can achieve better texture suppression performance.